Chapter 0: Preliminaries

Size: px
Start display at page:

Download "Chapter 0: Preliminaries"

Transcription

1 Chapter 0: Preliminaries Adam Sheffer March 28, Notation This chapter briefly surveys some basic concepts and tools that we will use in this class. Graduate students and some undergrads would probably be familiar with most or all of these. Such students may prefer to skip this chapter and only refer to it when encountering an unfamiliar concept. We use standard asymptotic notation. That is, fn Ogn implies that there exist constants c, n 0, such that for any n n 0, we have fn c gn. For example, 10n On 2 holds since we can take c 100 and n Similarly, fn Ωgn implies that there exist constants c, n 0, such that for any n n 0, we have fn c gn. Finally, fn Θgn implies that both fn Ogn and fn Ωgn hold. The O -notation is similar to the O -notation, except that it ignores polylogarithmic factors. That is, fn Ogn implies that there exist a constant n 0 and c that may have polylogarithmic dependence in n, such that for any n n 0, we have fn c gn. For example, 10n 2 lg 5 n lg lg n O n 2. We define Ω and Θ in a symmetric manner. Similarly, when writing an expression of the form O s,t, we mean that the hidden constant may depend on the variables s and t. For example, 10s 100 n 2 + s t100s O s,t n 2. We use standard graph theoretic notation. We usually denote a graph as G V, E. We denote a bipartite graph as G V U, E, where V and U are the two vertex sets. Given a graph G V, E and a vertex v V, we set Nv {u V : v, u E} that is, Nv is the set of neighbors of v. For two disjoint subsets A, B V, we set EA, B {u, v E : u A and v B} that is, the set of edges that connect the two subsets. We denote the expectation of a random variable X as E[X]. This is to prevent confusion between expectation and sets that are denoted as E. 1

2 We will often rely on the two following inequalities. Claim 1.1 The Cauchy-Schwarz inequality. Given any two sequences of real numbers a 1,..., a n and b 1,..., b n, we have n 2 n a j b j a 2 j n Claim 1.2 Hölder s inequality. Let p and q be two positive real numbers that satisfy 1/p + 1/q 1. Then for any two sequences of real numbers a 1,..., a n and b 1,..., b n, we have 2 Groups n n 1/p n 1/q a j b j a j p b j q. A group G consists of a set and a binary operation. Abusing notation, we will usually also refer to the set as G. We will usually denote the operation as +, even though this might be an operation that is very different from addition. For G to be a group under the operation +, it needs to satisfy the following properties: Closure. For every a, b G we have a + b G. Associativity. For every a, b, c G we have a + b + c a + b + c. Identity element. There exists an element 0 G such that for every a G we have a a a. This element is called the identity element. Inverse elements. For every a G there exists an element a G such that a + a a + a 0. This element is called the inverse of a. Let us consider a few simple examples of groups. The set of integers is a group under addition. It is obviously closed and associative, the identity element is 0, and the inverse of an integer a is a. The set of integers is not a group under multiplication. It is closed, associative, and has the identity element 1. However, most of the integers do not have an inverse. For example, no integer x satisfies 2 x x 2 1. The set of even integers is a group under addition. The set of odd integers is not, since it is not closed and does not contain an identity element. b 2 j. 2

3 For a positive integer n, the set {0, 1, 2,..., n 1} under addition mod n is a group. The identity element is 0 and the inverse of a is n a. The set of non-zero rational numbers Q \ {0} is a group under multiplication. It is obviously closed and associative, the identity element is 1, and the inverse of a is 1/a. The set of real numbers R under standard addition is a group. The set R \ {0} is a group under multiplication. By using inverse elements we can define the inverse operation, denoted as -. Specifically, given a group G and a, b G, we define a b as a + b as before, the notation - might be a bit misleading since this operation might be very different from subtraction. Notice that being a group under + does not necessarily imply being a group under -. For example, while Q \ {0} is a group under multiplication, it is not a group under the inverse operation of division. Let G be a group. A subgroup G of G is a group under the same operation as G and with a subset of the elements G G. For example, the even integers under addition is a subgroup of the group of integers under addition. The set of integers that are divisible by four is in turn a subgroup of the group of even integers both under addition. Claim 2.1. Let G be a group and let A G be closed under the inverse operation - that is, for any a, b A we have a b A. Then A is a subgroup of G under the original operation +. Proof. We need to prove that A satisfies the four basic properties of a group. First, since G is associative under + we have that A is associative under +. Consider an element a A and note that by definition a a A. Since a a is the identity element 0, we get that 0 A. For any a A, we have that 0 a A. By the definition of the identity element, 0 a is the inverse of a. That is, for any a A, the inverse element a is also in A. Finally, if a, b A then by the inverse property b A. By the definition of A we have a b A, so a + b A. This establishes that A has the closure property and completes the proof. A group G is said to be abelian or commutative if for every a, b G we have a + b b + a. All of the groups there were mentioned above are abelian, and these lecture notes will only deal with abelian groups. As an example of a non-abelian group, consider the set of n n matrices with entries in R and a determinant of 1. This set is a group under standard matrix multiplication. One can verify that matrix 3

4 multiplication is associative, that the product of two n n matrices with determinant 1 results in an n n matrix with determinant 1, that the identity element is the n n identity matrix, and that any matrix with a non-zero determinant has an inverse. However, this group is not abelian. For example, we have and Given a group G and a G \ {0}, the order of a is the smallest positive integer k that satisfies k times {}}{ a + a + + a 0 where 0 is the identity element of G. For example, consider the group {0, 1, 2,..., n 1} under addition mod n, where n is an even integer larger than 2. In this group the element 1 has an order of n and the element 2 has an order of n/2. In the group of integers under standard addition, every non-identity element has an infinite order. Let H be a subgroup of an abelian group G. The coset of H with respect to an element a G is a + H {b G : b a h for some g H}. Notice that when a H we get a + H H. As an example, let G be the set of integers under standard addition and let H be the subgroup of even integers. Then the coset of h with respect to the element 1 G is the subset of odd integers. Notice that 1 + H is not a subgroup. 1 Consider two groups G 1 and G 2 under the respective operations + 1 and + 2. The direct product G 1 G 2 is the set G 1 G 2 under the following operation +. Given a 1, b 1, G 1 and a 2, b 2 G 2, we set a 1, b 1 + a 2, b 2 a b 1, a b 2. Claim 2.2. Consider two groups G 1 and G 2 under respective operations + 1 and + 2. Then G 1 G 2 is a group. Proof. Consider three elements a 1, a 2, b 1, b 2, c 1, c 2 G 1 G 2. By definition, we have a 1, a 2 + b 1, b 2 a b 1, a b 2. The closure of G 1 and G 2 implies that a b 1, a b 2 G 1 G 2, which establishes the closure of G 1 G 2. By the associativity of G 1 and G 2, we have a b c 1, a b c 2 a 1, a 2 +b 1, b 2 + c 1, c 2 a b c 1, a b c 2 a 1, b 1 + b 1, b 2 + c 1, c 2. 1 Usually one separately considers left cosets a + H and right cosets H + a. Since we will only work with abelian groups this distinction is meaningless. 4

5 Denote the identity elements of G 1 and G 2 as 0 1 and 0 2, respectively. The identity element of G 1 G 2 is 0 1, 0 2, since 0 1, a 1, a 2 a 1, a , 0 2 a 1, a 2. Finally, the inverse of a 1, a 2 is a 1, a 2 where a j is the inverse of a j. Indeed, a 1, a 2 + a 1, a 2 0 1, 0 2. Given groups G 1, G 2,..., G n, the direct product G 1 G 2 G n is a group by applying Claim 2.2 iteratively. For example, by taking every G j to be the group of real numbers under addition, we obtain the real space R n R R R. That is, the set R n is a group under coordinatewise addition. A group that will be very useful to us is the set {0, 1, 2,..., n 1} under addition mod n for a positive integer m. We refer to this group as F n. For a positive integer m, we set F m n F n F n F n m times. For more basic details about groups, see for example [1]. References [1] J. J. Rotman, A first course in abstract algebra, Pearson Prentice Hall,

Lecture 2. Fundamentals of the Analysis of Algorithm Efficiency

Lecture 2. Fundamentals of the Analysis of Algorithm Efficiency Lecture 2 Fundamentals of the Analysis of Algorithm Efficiency 1 Lecture Contents 1. Analysis Framework 2. Asymptotic Notations and Basic Efficiency Classes 3. Mathematical Analysis of Nonrecursive Algorithms

More information

3.1 Asymptotic notation

3.1 Asymptotic notation 3.1 Asymptotic notation The notations we use to describe the asymptotic running time of an algorithm are defined in terms of functions whose domains are the set of natural numbers N = {0, 1, 2,... Such

More information

Big O 2/14/13. Administrative. Does it terminate? David Kauchak cs302 Spring 2013

Big O 2/14/13. Administrative. Does it terminate? David Kauchak cs302 Spring 2013 /4/3 Administrative Big O David Kauchak cs3 Spring 3 l Assignment : how d it go? l Assignment : out soon l CLRS code? l Videos Insertion-sort Insertion-sort Does it terminate? /4/3 Insertion-sort Loop

More information

with the size of the input in the limit, as the size of the misused.

with the size of the input in the limit, as the size of the misused. Chapter 3. Growth of Functions Outline Study the asymptotic efficiency of algorithms Give several standard methods for simplifying the asymptotic analysis of algorithms Present several notational conventions

More information

MATH 433 Applied Algebra Lecture 22: Review for Exam 2.

MATH 433 Applied Algebra Lecture 22: Review for Exam 2. MATH 433 Applied Algebra Lecture 22: Review for Exam 2. Topics for Exam 2 Permutations Cycles, transpositions Cycle decomposition of a permutation Order of a permutation Sign of a permutation Symmetric

More information

Introduction to Algorithms

Introduction to Algorithms Introduction to Algorithms (2 nd edition) by Cormen, Leiserson, Rivest & Stein Chapter 3: Growth of Functions (slides enhanced by N. Adlai A. DePano) Overview Order of growth of functions provides a simple

More information

CSCE 222 Discrete Structures for Computing. Review for Exam 2. Dr. Hyunyoung Lee !!!

CSCE 222 Discrete Structures for Computing. Review for Exam 2. Dr. Hyunyoung Lee !!! CSCE 222 Discrete Structures for Computing Review for Exam 2 Dr. Hyunyoung Lee 1 Strategy for Exam Preparation - Start studying now (unless have already started) - Study class notes (lecture slides and

More information

Definition List Modern Algebra, Fall 2011 Anders O.F. Hendrickson

Definition List Modern Algebra, Fall 2011 Anders O.F. Hendrickson Definition List Modern Algebra, Fall 2011 Anders O.F. Hendrickson On almost every Friday of the semester, we will have a brief quiz to make sure you have memorized the definitions encountered in our studies.

More information

Ma/CS 6b Class 25: Error Correcting Codes 2

Ma/CS 6b Class 25: Error Correcting Codes 2 Ma/CS 6b Class 25: Error Correcting Codes 2 By Adam Sheffer Recall: Codes V n the set of binary sequences of length n. For example, V 3 = 000,001,010,011,100,101,110,111. Codes of length n are subsets

More information

5 Group theory. 5.1 Binary operations

5 Group theory. 5.1 Binary operations 5 Group theory This section is an introduction to abstract algebra. This is a very useful and important subject for those of you who will continue to study pure mathematics. 5.1 Binary operations 5.1.1

More information

Algebraic Structures Exam File Fall 2013 Exam #1

Algebraic Structures Exam File Fall 2013 Exam #1 Algebraic Structures Exam File Fall 2013 Exam #1 1.) Find all four solutions to the equation x 4 + 16 = 0. Give your answers as complex numbers in standard form, a + bi. 2.) Do the following. a.) Write

More information

Math 3121, A Summary of Sections 0,1,2,4,5,6,7,8,9

Math 3121, A Summary of Sections 0,1,2,4,5,6,7,8,9 Math 3121, A Summary of Sections 0,1,2,4,5,6,7,8,9 Section 0. Sets and Relations Subset of a set, B A, B A (Definition 0.1). Cartesian product of sets A B ( Defintion 0.4). Relation (Defintion 0.7). Function,

More information

Eigenvalues, random walks and Ramanujan graphs

Eigenvalues, random walks and Ramanujan graphs Eigenvalues, random walks and Ramanujan graphs David Ellis 1 The Expander Mixing lemma We have seen that a bounded-degree graph is a good edge-expander if and only if if has large spectral gap If G = (V,

More information

0 Sets and Induction. Sets

0 Sets and Induction. Sets 0 Sets and Induction Sets A set is an unordered collection of objects, called elements or members of the set. A set is said to contain its elements. We write a A to denote that a is an element of the set

More information

Definitions. Notations. Injective, Surjective and Bijective. Divides. Cartesian Product. Relations. Equivalence Relations

Definitions. Notations. Injective, Surjective and Bijective. Divides. Cartesian Product. Relations. Equivalence Relations Page 1 Definitions Tuesday, May 8, 2018 12:23 AM Notations " " means "equals, by definition" the set of all real numbers the set of integers Denote a function from a set to a set by Denote the image of

More information

Module 1: Analyzing the Efficiency of Algorithms

Module 1: Analyzing the Efficiency of Algorithms Module 1: Analyzing the Efficiency of Algorithms Dr. Natarajan Meghanathan Professor of Computer Science Jackson State University Jackson, MS 39217 E-mail: natarajan.meghanathan@jsums.edu What is an Algorithm?

More information

BASIC GROUP THEORY : G G G,

BASIC GROUP THEORY : G G G, BASIC GROUP THEORY 18.904 1. Definitions Definition 1.1. A group (G, ) is a set G with a binary operation : G G G, and a unit e G, possessing the following properties. (1) Unital: for g G, we have g e

More information

Data Structures and Algorithms Running time and growth functions January 18, 2018

Data Structures and Algorithms Running time and growth functions January 18, 2018 Data Structures and Algorithms Running time and growth functions January 18, 2018 Measuring Running Time of Algorithms One way to measure the running time of an algorithm is to implement it and then study

More information

Solutions to Assignment 3

Solutions to Assignment 3 Solutions to Assignment 3 Question 1. [Exercises 3.1 # 2] Let R = {0 e b c} with addition multiplication defined by the following tables. Assume associativity distributivity show that R is a ring with

More information

Ma/CS 6a Class 19: Group Isomorphisms

Ma/CS 6a Class 19: Group Isomorphisms Ma/CS 6a Class 19: Group Isomorphisms By Adam Sheffer A Group A group consists of a set G and a binary operation, satisfying the following. Closure. For every x, y G x y G. Associativity. For every x,

More information

* 8 Groups, with Appendix containing Rings and Fields.

* 8 Groups, with Appendix containing Rings and Fields. * 8 Groups, with Appendix containing Rings and Fields Binary Operations Definition We say that is a binary operation on a set S if, and only if, a, b, a b S Implicit in this definition is the idea that

More information

1. Introduction to commutative rings and fields

1. Introduction to commutative rings and fields 1. Introduction to commutative rings and fields Very informally speaking, a commutative ring is a set in which we can add, subtract and multiply elements so that the usual laws hold. A field is a commutative

More information

Grade 11/12 Math Circles Fall Nov. 5 Recurrences, Part 2

Grade 11/12 Math Circles Fall Nov. 5 Recurrences, Part 2 1 Faculty of Mathematics Waterloo, Ontario Centre for Education in Mathematics and Computing Grade 11/12 Math Circles Fall 2014 - Nov. 5 Recurrences, Part 2 Running time of algorithms In computer science,

More information

Algorithm efficiency can be measured in terms of: Time Space Other resources such as processors, network packets, etc.

Algorithm efficiency can be measured in terms of: Time Space Other resources such as processors, network packets, etc. Algorithms Analysis Algorithm efficiency can be measured in terms of: Time Space Other resources such as processors, network packets, etc. Algorithms analysis tends to focus on time: Techniques for measuring

More information

A Generalization of Wilson s Theorem

A Generalization of Wilson s Theorem A Generalization of Wilson s Theorem R. Andrew Ohana June 3, 2009 Contents 1 Introduction 2 2 Background Algebra 2 2.1 Groups................................. 2 2.2 Rings.................................

More information

Many of the groups with which we are familiar are arithmetical in nature, and they tend to share key structures that combine more than one operation.

Many of the groups with which we are familiar are arithmetical in nature, and they tend to share key structures that combine more than one operation. 12. Rings 1 Rings Many of the groups with which we are familiar are arithmetical in nature, and they tend to share key structures that combine more than one operation. Example: Z, Q, R, and C are an Abelian

More information

Analysis of Algorithm Efficiency. Dr. Yingwu Zhu

Analysis of Algorithm Efficiency. Dr. Yingwu Zhu Analysis of Algorithm Efficiency Dr. Yingwu Zhu Measure Algorithm Efficiency Time efficiency How fast the algorithm runs; amount of time required to accomplish the task Our focus! Space efficiency Amount

More information

When we use asymptotic notation within an expression, the asymptotic notation is shorthand for an unspecified function satisfying the relation:

When we use asymptotic notation within an expression, the asymptotic notation is shorthand for an unspecified function satisfying the relation: CS 124 Section #1 Big-Oh, the Master Theorem, and MergeSort 1/29/2018 1 Big-Oh Notation 1.1 Definition Big-Oh notation is a way to describe the rate of growth of functions. In CS, we use it to describe

More information

Abstract Algebra, HW6 Solutions. Chapter 5

Abstract Algebra, HW6 Solutions. Chapter 5 Abstract Algebra, HW6 Solutions Chapter 5 6 We note that lcm(3,5)15 So, we need to come up with two disjoint cycles of lengths 3 and 5 The obvious choices are (13) and (45678) So if we consider the element

More information

Review 1. Andreas Klappenecker

Review 1. Andreas Klappenecker Review 1 Andreas Klappenecker Summary Propositional Logic, Chapter 1 Predicate Logic, Chapter 1 Proofs, Chapter 1 Sets, Chapter 2 Functions, Chapter 2 Sequences and Sums, Chapter 2 Asymptotic Notations,

More information

1.3 Vertex Degrees. Vertex Degree for Undirected Graphs: Let G be an undirected. Vertex Degree for Digraphs: Let D be a digraph and y V (D).

1.3 Vertex Degrees. Vertex Degree for Undirected Graphs: Let G be an undirected. Vertex Degree for Digraphs: Let D be a digraph and y V (D). 1.3. VERTEX DEGREES 11 1.3 Vertex Degrees Vertex Degree for Undirected Graphs: Let G be an undirected graph and x V (G). The degree d G (x) of x in G: the number of edges incident with x, each loop counting

More information

(i) Write down the elements of a subgroup of order 2. [1]

(i) Write down the elements of a subgroup of order 2. [1] FP3 Groups 1. June 010 qu. A multiplicative group with identity e contains distinct elements a and r, with the properties r 6 = e and = r 5 a. Prove that r = a. [] Prove, by induction or otherwise, that

More information

Foundations of Cryptography

Foundations of Cryptography Foundations of Cryptography Ville Junnila viljun@utu.fi Department of Mathematics and Statistics University of Turku 2015 Ville Junnila viljun@utu.fi Lecture 7 1 of 18 Cosets Definition 2.12 Let G be a

More information

The Completion of a Metric Space

The Completion of a Metric Space The Completion of a Metric Space Let (X, d) be a metric space. The goal of these notes is to construct a complete metric space which contains X as a subspace and which is the smallest space with respect

More information

A Little Beyond: Linear Algebra

A Little Beyond: Linear Algebra A Little Beyond: Linear Algebra Akshay Tiwary March 6, 2016 Any suggestions, questions and remarks are welcome! 1 A little extra Linear Algebra 1. Show that any set of non-zero polynomials in [x], no two

More information

INTRODUCTION TO THE GROUP THEORY

INTRODUCTION TO THE GROUP THEORY Lecture Notes on Structure of Algebra INTRODUCTION TO THE GROUP THEORY By : Drs. Antonius Cahya Prihandoko, M.App.Sc e-mail: antoniuscp.fkip@unej.ac.id Mathematics Education Study Program Faculty of Teacher

More information

Tomáš Madaras Congruence classes

Tomáš Madaras Congruence classes Congruence classes For given integer m 2, the congruence relation modulo m at the set Z is the equivalence relation, thus, it provides a corresponding partition of Z into mutually disjoint sets. Definition

More information

Ma/CS 6a Class 28: Latin Squares

Ma/CS 6a Class 28: Latin Squares Ma/CS 6a Class 28: Latin Squares By Adam Sheffer Latin Squares A Latin square is an n n array filled with n different symbols, each occurring exactly once in each row and exactly once in each column. 1

More information

FINITE ABELIAN GROUPS Amin Witno

FINITE ABELIAN GROUPS Amin Witno WON Series in Discrete Mathematics and Modern Algebra Volume 7 FINITE ABELIAN GROUPS Amin Witno Abstract We detail the proof of the fundamental theorem of finite abelian groups, which states that every

More information

Algebraic structures I

Algebraic structures I MTH5100 Assignment 1-10 Algebraic structures I For handing in on various dates January March 2011 1 FUNCTIONS. Say which of the following rules successfully define functions, giving reasons. For each one

More information

Asymptotic Analysis 1

Asymptotic Analysis 1 Asymptotic Analysis 1 Last week, we discussed how to present algorithms using pseudocode. For example, we looked at an algorithm for singing the annoying song 99 Bottles of Beer on the Wall for arbitrary

More information

Mathematics for Cryptography

Mathematics for Cryptography Mathematics for Cryptography Douglas R. Stinson David R. Cheriton School of Computer Science University of Waterloo Waterloo, Ontario, N2L 3G1, Canada March 15, 2016 1 Groups and Modular Arithmetic 1.1

More information

Algorithms, CSE, OSU. Introduction, complexity of algorithms, asymptotic growth of functions. Instructor: Anastasios Sidiropoulos

Algorithms, CSE, OSU. Introduction, complexity of algorithms, asymptotic growth of functions. Instructor: Anastasios Sidiropoulos 6331 - Algorithms, CSE, OSU Introduction, complexity of algorithms, asymptotic growth of functions Instructor: Anastasios Sidiropoulos Why algorithms? Algorithms are at the core of Computer Science Why

More information

When we use asymptotic notation within an expression, the asymptotic notation is shorthand for an unspecified function satisfying the relation:

When we use asymptotic notation within an expression, the asymptotic notation is shorthand for an unspecified function satisfying the relation: CS 124 Section #1 Big-Oh, the Master Theorem, and MergeSort 1/29/2018 1 Big-Oh Notation 1.1 Definition Big-Oh notation is a way to describe the rate of growth of functions. In CS, we use it to describe

More information

Ma/CS 6a Class 28: Latin Squares

Ma/CS 6a Class 28: Latin Squares Ma/CS 6a Class 28: Latin Squares By Adam Sheffer Latin Squares A Latin square is an n n array filled with n different symbols, each occurring exactly once in each row and exactly once in each column. 1

More information

CSC Design and Analysis of Algorithms. Lecture 1

CSC Design and Analysis of Algorithms. Lecture 1 CSC 8301- Design and Analysis of Algorithms Lecture 1 Introduction Analysis framework and asymptotic notations What is an algorithm? An algorithm is a finite sequence of unambiguous instructions for solving

More information

Error Correcting Codes Prof. Dr. P Vijay Kumar Department of Electrical Communication Engineering Indian Institute of Science, Bangalore

Error Correcting Codes Prof. Dr. P Vijay Kumar Department of Electrical Communication Engineering Indian Institute of Science, Bangalore (Refer Slide Time: 00:54) Error Correcting Codes Prof. Dr. P Vijay Kumar Department of Electrical Communication Engineering Indian Institute of Science, Bangalore Lecture No. # 05 Cosets, Rings & Fields

More information

ORDERS OF ELEMENTS IN A GROUP

ORDERS OF ELEMENTS IN A GROUP ORDERS OF ELEMENTS IN A GROUP KEITH CONRAD 1. Introduction Let G be a group and g G. We say g has finite order if g n = e for some positive integer n. For example, 1 and i have finite order in C, since

More information

CSCE 222 Discrete Structures for Computing. Review for the Final. Hyunyoung Lee

CSCE 222 Discrete Structures for Computing. Review for the Final. Hyunyoung Lee CSCE 222 Discrete Structures for Computing Review for the Final! Hyunyoung Lee! 1 Final Exam Section 501 (regular class time 8:00am) Friday, May 8, starting at 1:00pm in our classroom!! Section 502 (regular

More information

MIT Algebraic techniques and semidefinite optimization February 16, Lecture 4

MIT Algebraic techniques and semidefinite optimization February 16, Lecture 4 MIT 6.972 Algebraic techniques and semidefinite optimization February 16, 2006 Lecture 4 Lecturer: Pablo A. Parrilo Scribe: Pablo A. Parrilo In this lecture we will review some basic elements of abstract

More information

Lectures 15: Cayley Graphs of Abelian Groups

Lectures 15: Cayley Graphs of Abelian Groups U.C. Berkeley CS294: Spectral Methods and Expanders Handout 15 Luca Trevisan March 14, 2016 Lectures 15: Cayley Graphs of Abelian Groups In which we show how to find the eigenvalues and eigenvectors of

More information

Ma/CS 6b Class 3: Stable Matchings

Ma/CS 6b Class 3: Stable Matchings Ma/CS 6b Class 3: Stable Matchings α p 5 p 12 p 15 q 1 q 7 q 12 β By Adam Sheffer Neighbor Sets Let G = V 1 V 2, E be a bipartite graph. For any vertex a V 1, we define the neighbor set of a as N a = u

More information

Exercises on chapter 1

Exercises on chapter 1 Exercises on chapter 1 1. Let G be a group and H and K be subgroups. Let HK = {hk h H, k K}. (i) Prove that HK is a subgroup of G if and only if HK = KH. (ii) If either H or K is a normal subgroup of G

More information

Module 1: Analyzing the Efficiency of Algorithms

Module 1: Analyzing the Efficiency of Algorithms Module 1: Analyzing the Efficiency of Algorithms Dr. Natarajan Meghanathan Associate Professor of Computer Science Jackson State University Jackson, MS 39217 E-mail: natarajan.meghanathan@jsums.edu Based

More information

ECEN 5022 Cryptography

ECEN 5022 Cryptography Elementary Algebra and Number Theory University of Colorado Spring 2008 Divisibility, Primes Definition. N denotes the set {1, 2, 3,...} of natural numbers and Z denotes the set of integers {..., 2, 1,

More information

Converse to Lagrange s Theorem Groups

Converse to Lagrange s Theorem Groups Converse to Lagrange s Theorem Groups Blain A Patterson Youngstown State University May 10, 2013 History In 1771 an Italian mathematician named Joseph Lagrange proved a theorem that put constraints on

More information

Zero-Sum Flows in Regular Graphs

Zero-Sum Flows in Regular Graphs Zero-Sum Flows in Regular Graphs S. Akbari,5, A. Daemi 2, O. Hatami, A. Javanmard 3, A. Mehrabian 4 Department of Mathematical Sciences Sharif University of Technology Tehran, Iran 2 Department of Mathematics

More information

a + b = b + a and a b = b a. (a + b) + c = a + (b + c) and (a b) c = a (b c). a (b + c) = a b + a c and (a + b) c = a c + b c.

a + b = b + a and a b = b a. (a + b) + c = a + (b + c) and (a b) c = a (b c). a (b + c) = a b + a c and (a + b) c = a c + b c. Properties of the Integers The set of all integers is the set and the subset of Z given by Z = {, 5, 4, 3, 2, 1, 0, 1, 2, 3, 4, 5, }, N = {0, 1, 2, 3, 4, }, is the set of nonnegative integers (also called

More information

chapter 12 MORE MATRIX ALGEBRA 12.1 Systems of Linear Equations GOALS

chapter 12 MORE MATRIX ALGEBRA 12.1 Systems of Linear Equations GOALS chapter MORE MATRIX ALGEBRA GOALS In Chapter we studied matrix operations and the algebra of sets and logic. We also made note of the strong resemblance of matrix algebra to elementary algebra. The reader

More information

Homework Assignment 1 Solutions

Homework Assignment 1 Solutions MTAT.03.286: Advanced Methods in Algorithms Homework Assignment 1 Solutions University of Tartu 1 Big-O notation For each of the following, indicate whether f(n) = O(g(n)), f(n) = Ω(g(n)), or f(n) = Θ(g(n)).

More information

Properties of the Integers

Properties of the Integers Properties of the Integers The set of all integers is the set and the subset of Z given by Z = {, 5, 4, 3, 2, 1, 0, 1, 2, 3, 4, 5, }, N = {0, 1, 2, 3, 4, }, is the set of nonnegative integers (also called

More information

1. Introduction to commutative rings and fields

1. Introduction to commutative rings and fields 1. Introduction to commutative rings and fields Very informally speaking, a commutative ring is a set in which we can add, subtract and multiply elements so that the usual laws hold. A field is a commutative

More information

Algebra Exercises in group theory

Algebra Exercises in group theory Algebra 3 2010 Exercises in group theory February 2010 Exercise 1*: Discuss the Exercises in the sections 1.1-1.3 in Chapter I of the notes. Exercise 2: Show that an infinite group G has to contain a non-trivial

More information

Introduction to finite fields

Introduction to finite fields Chapter 7 Introduction to finite fields This chapter provides an introduction to several kinds of abstract algebraic structures, particularly groups, fields, and polynomials. Our primary interest is in

More information

DEPARTMENT OF MATHEMATIC EDUCATION MATHEMATIC AND NATURAL SCIENCE FACULTY

DEPARTMENT OF MATHEMATIC EDUCATION MATHEMATIC AND NATURAL SCIENCE FACULTY HANDOUT ABSTRACT ALGEBRA MUSTHOFA DEPARTMENT OF MATHEMATIC EDUCATION MATHEMATIC AND NATURAL SCIENCE FACULTY 2012 BINARY OPERATION We are all familiar with addition and multiplication of two numbers. Both

More information

Discrete Structures for Computer Science: Counting, Recursion, and Probability

Discrete Structures for Computer Science: Counting, Recursion, and Probability Discrete Structures for Computer Science: Counting, Recursion, and Probability Michiel Smid School of Computer Science Carleton University Ottawa, Ontario Canada michiel@scs.carleton.ca December 18, 2017

More information

Preliminaries and Complexity Theory

Preliminaries and Complexity Theory Preliminaries and Complexity Theory Oleksandr Romanko CAS 746 - Advanced Topics in Combinatorial Optimization McMaster University, January 16, 2006 Introduction Book structure: 2 Part I Linear Algebra

More information

CS Non-recursive and Recursive Algorithm Analysis

CS Non-recursive and Recursive Algorithm Analysis CS483-04 Non-recursive and Recursive Algorithm Analysis Instructor: Fei Li Room 443 ST II Office hours: Tue. & Thur. 4:30pm - 5:30pm or by appointments lifei@cs.gmu.edu with subject: CS483 http://www.cs.gmu.edu/

More information

Extra exercises for algebra

Extra exercises for algebra Extra exercises for algebra These are extra exercises for the course algebra. They are meant for those students who tend to have already solved all the exercises at the beginning of the exercise session

More information

bc7f2306 Page 1 Name:

bc7f2306 Page 1 Name: Name: Questions 1 through 4 refer to the following: Solve the given inequality and represent the solution set using set notation: 1) 3x 1 < 2(x + 4) or 7x 3 2(x + 1) Questions 5 and 6 refer to the following:

More information

7. Let K = 15 be the subgroup of G = Z generated by 15. (a) List the elements of K = 15. Answer: K = 15 = {15k k Z} (b) Prove that K is normal subgroup of G. Proof: (Z +) is Abelian group and any subgroup

More information

Math 430 Exam 2, Fall 2008

Math 430 Exam 2, Fall 2008 Do not distribute. IIT Dept. Applied Mathematics, February 16, 2009 1 PRINT Last name: Signature: First name: Student ID: Math 430 Exam 2, Fall 2008 These theorems may be cited at any time during the test

More information

MATH 430 PART 2: GROUPS AND SUBGROUPS

MATH 430 PART 2: GROUPS AND SUBGROUPS MATH 430 PART 2: GROUPS AND SUBGROUPS Last class, we encountered the structure D 3 where the set was motions which preserve an equilateral triangle and the operation was function composition. We determined

More information

Number Theory, Algebra and Analysis. William Yslas Vélez Department of Mathematics University of Arizona

Number Theory, Algebra and Analysis. William Yslas Vélez Department of Mathematics University of Arizona Number Theory, Algebra and Analysis William Yslas Vélez Department of Mathematics University of Arizona O F denotes the ring of integers in the field F, it mimics Z in Q How do primes factor as you consider

More information

AN ALGEBRA PRIMER WITH A VIEW TOWARD CURVES OVER FINITE FIELDS

AN ALGEBRA PRIMER WITH A VIEW TOWARD CURVES OVER FINITE FIELDS AN ALGEBRA PRIMER WITH A VIEW TOWARD CURVES OVER FINITE FIELDS The integers are the set 1. Groups, Rings, and Fields: Basic Examples Z := {..., 3, 2, 1, 0, 1, 2, 3,...}, and we can add, subtract, and multiply

More information

Mathematics 222a Quiz 2 CODE 111 November 21, 2002

Mathematics 222a Quiz 2 CODE 111 November 21, 2002 Student s Name [print] Student Number Mathematics 222a Instructions: Print your name and student number at the top of this question sheet. Print your name and your instructor s name on the answer sheet.

More information

2) e = e G G such that if a G 0 =0 G G such that if a G e a = a e = a. 0 +a = a+0 = a.

2) e = e G G such that if a G 0 =0 G G such that if a G e a = a e = a. 0 +a = a+0 = a. Chapter 2 Groups Groups are the central objects of algebra. In later chapters we will define rings and modules and see that they are special cases of groups. Also ring homomorphisms and module homomorphisms

More information

1. Revision Description Reflect and Review Teasers Answers Recall of Rational Numbers:

1. Revision Description Reflect and Review Teasers Answers Recall of Rational Numbers: 1. Revision Description Reflect Review Teasers Answers Recall of Rational Numbers: A rational number is of the form, where p q are integers q 0. Addition or subtraction of rational numbers is possible

More information

Growth of Functions (CLRS 2.3,3)

Growth of Functions (CLRS 2.3,3) Growth of Functions (CLRS 2.3,3) 1 Review Last time we discussed running time of algorithms and introduced the RAM model of computation. Best-case running time: the shortest running time for any input

More information

THE REAL NUMBERS Chapter #4

THE REAL NUMBERS Chapter #4 FOUNDATIONS OF ANALYSIS FALL 2008 TRUE/FALSE QUESTIONS THE REAL NUMBERS Chapter #4 (1) Every element in a field has a multiplicative inverse. (2) In a field the additive inverse of 1 is 0. (3) In a field

More information

FROM GROUPS TO GALOIS Amin Witno

FROM GROUPS TO GALOIS Amin Witno WON Series in Discrete Mathematics and Modern Algebra Volume 6 FROM GROUPS TO GALOIS Amin Witno These notes 1 have been prepared for the students at Philadelphia University (Jordan) who are taking the

More information

CS473 - Algorithms I

CS473 - Algorithms I CS473 - Algorithms I Lecture 2 Asymptotic Notation 1 O-notation: Asymptotic upper bound f(n) = O(g(n)) if positive constants c, n 0 such that 0 f(n) cg(n), n n 0 f(n) = O(g(n)) cg(n) f(n) Asymptotic running

More information

DS-GA 1002 Lecture notes 0 Fall Linear Algebra. These notes provide a review of basic concepts in linear algebra.

DS-GA 1002 Lecture notes 0 Fall Linear Algebra. These notes provide a review of basic concepts in linear algebra. DS-GA 1002 Lecture notes 0 Fall 2016 Linear Algebra These notes provide a review of basic concepts in linear algebra. 1 Vector spaces You are no doubt familiar with vectors in R 2 or R 3, i.e. [ ] 1.1

More information

ACO Comprehensive Exam March 17 and 18, Computability, Complexity and Algorithms

ACO Comprehensive Exam March 17 and 18, Computability, Complexity and Algorithms 1. Computability, Complexity and Algorithms (a) Let G(V, E) be an undirected unweighted graph. Let C V be a vertex cover of G. Argue that V \ C is an independent set of G. (b) Minimum cardinality vertex

More information

Algebra: Groups. Group Theory a. Examples of Groups. groups. The inverse of a is simply a, which exists.

Algebra: Groups. Group Theory a. Examples of Groups. groups. The inverse of a is simply a, which exists. Group Theory a Let G be a set and be a binary operation on G. (G, ) is called a group if it satisfies the following. 1. For all a, b G, a b G (closure). 2. For all a, b, c G, a (b c) = (a b) c (associativity).

More information

Analysis of Algorithms

Analysis of Algorithms October 1, 2015 Analysis of Algorithms CS 141, Fall 2015 1 Analysis of Algorithms: Issues Correctness/Optimality Running time ( time complexity ) Memory requirements ( space complexity ) Power I/O utilization

More information

Monoids. Definition: A binary operation on a set M is a function : M M M. Examples:

Monoids. Definition: A binary operation on a set M is a function : M M M. Examples: Monoids Definition: A binary operation on a set M is a function : M M M. If : M M M, we say that is well defined on M or equivalently, that M is closed under the operation. Examples: Definition: A monoid

More information

Ma/CS 6b Class 3: Stable Matchings

Ma/CS 6b Class 3: Stable Matchings Ma/CS 6b Class 3: Stable Matchings α p 5 p 12 p 15 q 1 q 7 q 12 By Adam Sheffer Reminder: Alternating Paths Let G = V 1 V 2, E be a bipartite graph, and let M be a matching of G. A path is alternating

More information

Rings If R is a commutative ring, a zero divisor is a nonzero element x such that xy = 0 for some nonzero element y R.

Rings If R is a commutative ring, a zero divisor is a nonzero element x such that xy = 0 for some nonzero element y R. Rings 10-26-2008 A ring is an abelian group R with binary operation + ( addition ), together with a second binary operation ( multiplication ). Multiplication must be associative, and must distribute over

More information

Lecture 4: Constructing the Integers, Rationals and Reals

Lecture 4: Constructing the Integers, Rationals and Reals Math/CS 20: Intro. to Math Professor: Padraic Bartlett Lecture 4: Constructing the Integers, Rationals and Reals Week 5 UCSB 204 The Integers Normally, using the natural numbers, you can easily define

More information

Principles of Real Analysis I Fall I. The Real Number System

Principles of Real Analysis I Fall I. The Real Number System 21-355 Principles of Real Analysis I Fall 2004 I. The Real Number System The main goal of this course is to develop the theory of real-valued functions of one real variable in a systematic and rigorous

More information

The Time Complexity of an Algorithm

The Time Complexity of an Algorithm CSE 3101Z Design and Analysis of Algorithms The Time Complexity of an Algorithm Specifies how the running time depends on the size of the input. Purpose To estimate how long a program will run. To estimate

More information

MATH 25 CLASS 21 NOTES, NOV Contents. 2. Subgroups 2 3. Isomorphisms 4

MATH 25 CLASS 21 NOTES, NOV Contents. 2. Subgroups 2 3. Isomorphisms 4 MATH 25 CLASS 21 NOTES, NOV 7 2011 Contents 1. Groups: definition 1 2. Subgroups 2 3. Isomorphisms 4 1. Groups: definition Even though we have been learning number theory without using any other parts

More information

Math 1302 Notes 2. How many solutions? What type of solution in the real number system? What kind of equation is it?

Math 1302 Notes 2. How many solutions? What type of solution in the real number system? What kind of equation is it? Math 1302 Notes 2 We know that x 2 + 4 = 0 has How many solutions? What type of solution in the real number system? What kind of equation is it? What happens if we enlarge our current system? Remember

More information

Part II. Number Theory. Year

Part II. Number Theory. Year Part II Year 2017 2016 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 2005 2017 Paper 3, Section I 1G 70 Explain what is meant by an Euler pseudoprime and a strong pseudoprime. Show that 65 is an Euler

More information

Asymptotic Analysis. Slides by Carl Kingsford. Jan. 27, AD Chapter 2

Asymptotic Analysis. Slides by Carl Kingsford. Jan. 27, AD Chapter 2 Asymptotic Analysis Slides by Carl Kingsford Jan. 27, 2014 AD Chapter 2 Independent Set Definition (Independent Set). Given a graph G = (V, E) an independent set is a set S V if no two nodes in S are joined

More information

Outline. We will now investigate the structure of this important set.

Outline. We will now investigate the structure of this important set. The Reals Outline As we have seen, the set of real numbers, R, has cardinality c. This doesn't tell us very much about the reals, since there are many sets with this cardinality and cardinality doesn't

More information

HOMEWORK Graduate Abstract Algebra I May 2, 2004

HOMEWORK Graduate Abstract Algebra I May 2, 2004 Math 5331 Sec 121 Spring 2004, UT Arlington HOMEWORK Graduate Abstract Algebra I May 2, 2004 The required text is Algebra, by Thomas W. Hungerford, Graduate Texts in Mathematics, Vol 73, Springer. (it

More information

Chapter 6: Third Moment Energy

Chapter 6: Third Moment Energy Chapter 6: Third Moment Energy Adam Sheffer May 13, 2016 1 Introduction In this chapter we will see how to obtain various results by relying on a generalization of the concept of energy. While such generalizations

More information

Unit I (Logic and Proofs)

Unit I (Logic and Proofs) SUBJECT NAME SUBJECT CODE : MA 6566 MATERIAL NAME REGULATION : Discrete Mathematics : Part A questions : R2013 UPDATED ON : April-May 2018 (Scan the above QR code for the direct download of this material)

More information